A lecture for UW EPI 519 providing background for genome-wide association studies, a few examples of recent papers in the CVD GWAS literature, and some lessons and new directions. The talk was originally given in 2008 (in collaboration with a colleagure), this version has been updated slightly for 2010 and includes references for further reading.
Some of the typefaces may have been mangled on conversion; the file download should be more reliable.
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2,000 human GWAS reports now appear in scientific journals. Our free eBook aims to explain the basic steps and concepts to complete a GWAS experiment.
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2,000 human GWAS reports now appear in scientific journals. Our free eBook aims to explain the basic steps and concepts to complete a GWAS experiment.
Association genetics‟ or ‟association studies,” or ‟linkage disequilibrium mapping”.
Tool to resolve complex trait variation down to the sequence level by exploiting historical and evolutionary recombination events at the population level.
Natural population surveyed to determine MTA using LD.
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
Linkage and QTL mapping Populations and Association mapping population.
F2, Immortalized F2, Backcross (BC), Near isogenic lines (NIL), RIL, Double haploids(DH), Nested Association mapping (NAM), MAGIC and Interconnected populations.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
Genome-wide association studies (GWAS) have been providing valuable insight to the genetics of common and complex diseases for many years. In this webcast we will walk through one possible workflow for completing GWAS in Golden Helix SNP & Variation Suite (SVS) with special attention paid to adjusting analysis for population stratification.
Association genetics‟ or ‟association studies,” or ‟linkage disequilibrium mapping”.
Tool to resolve complex trait variation down to the sequence level by exploiting historical and evolutionary recombination events at the population level.
Natural population surveyed to determine MTA using LD.
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
Linkage and QTL mapping Populations and Association mapping population.
F2, Immortalized F2, Backcross (BC), Near isogenic lines (NIL), RIL, Double haploids(DH), Nested Association mapping (NAM), MAGIC and Interconnected populations.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
Genome-wide association studies (GWAS) have been providing valuable insight to the genetics of common and complex diseases for many years. In this webcast we will walk through one possible workflow for completing GWAS in Golden Helix SNP & Variation Suite (SVS) with special attention paid to adjusting analysis for population stratification.
The slides of the talk of @PhilippBayer and I gave on the 28th Chaos Communication Congress. Sources can be found here: https://github.com/drsnuggles/opensnp28c3
See text at http://molcyt.org/2012/11/29/superdomestication-feed-forward-breeding-and-climate-proofing-crops/ which also links the the YouTube talk using these slides
It is widely agreed that complex diseases are typically caused by joint effects of multiple genetic variations, rather than a single genetic variation. Multi-SNP interactions, also known as epistatic interactions, have the potential to provide information about causes of complex diseases, and build on GWAS studies that look at associations between single SNPs and phenotypes. However, epistatic analysis methods are both computationally expensive, and have limited accessibility for biologists wanting to analyse GWAS datasets due to being command line based. Here we present APPistatic, a prototype desktop version of a pipeline for epistatic analysis of GWAS datasets. his application combines ease-of-use, via a GUI, with accelerated implementation of BOOST and FaST-LMM epistatic analysis methods.
Innovative research is done mainly by taxpayer funded research – government and universities funded by the NIH usually in universities and government labs and now in smaller biotech companies and then they license those to big drug companies.
Lecture given for the Data Mining course at Uppsala university in October 2013. The presentation talks about data analysis in the context of genomics, next-generation sequencing, metagenomics etc.
" we want to share with other interested colleagues,our adventure into
surprises of rotator cuff tendinopathy; obviously a cooperative effort could
give more consistent data "
Manuel Branes
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
1. Genome-wide
Association
Studies
EPI 519
21 October 2010
Joshua C. Bis, PhD
University of Washington, Cardiovascular
The Type 1 Diabetes Genetics Consortium. Nature Genetics, 2009 May 10
Health Research Unit
6. highly consistent associations*
Trait Gene Polymorphism Frequency
Deep Vein F5 Arg506Gln 0.015
Thrombosis
Graves’ disease CTLA4 Thr17Ala 0.62
Type 1 diabetes INS 5’VNTR 0.67
HIV infection CCR5 32 bp Ins/Del 0.05-0.07
Alzheimer’s disease APOE Epsilon 2/3/4 0.16-0.24
Creutzfelt-Jakob PRNP Met129Val 0.37
* Associations between polymorphisms and disease where at least 75% of identified studies
achieved statistical significance. (out of 600 gene–disease studies reviewed)
Hirschhorn: Genet Med, Volume 4(2).March/April 2002.45-61
7. “genomics”
The field within genetics
concerned with the structure and
function of the entire DNA
sequence of an individual or
population.
-- Thomas Roderick
McDonald’s Raw Bar
1986
8. genome-wide association study
“… a study of common genetic
variation across the entire
human genome designed to
identify genetic associations with
observable traits.”
-- National Institutes of Health,
“Policy for sharing of data obtained in
NIH-sponsored or conducted GWAS”
9. “A major strength of the
genome-wide approach … has
been its freedom from reliance
on prior knowledge.”
-- “A HapMap harvest of insights into the genetics of
common disease”
(Manolio, Brooks, Collins.)
13. haplotypes
The International HapMap Consortium. Nature | Vol 437 | 27October2005
14. “… to create a public, genome-
wide database of common
human sequence variation,
providing information needed as
a guide to genetic studies of
clinical phenotypes.”
-- October 2002
16. imputation
Use patterns of variation from HapMap to impute genotypes.
Increases power by allowing for association testing at
untyped markers and allows comparisons across studies and
platforms by using a common set of SNPs.
Li, Willer, Sanna, Abecasis. Annu Rev Genomics Hum Genet. 2009;10:387-406
22. association study
controls cases
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23. Manhattan plot
(McCarthy et al.,Nature Reviews Genetics, May 2008)
24. p-value
the probability of seeing your data or more extreme
data if the null hypothesis is true.
By chance, with 1,000,000 statistical tests:
• a threshold of p=0.05
would show 50,000 “significant” associations
360 cases : 360 controls
• a threshold of p = 0.05/1,000,000 (5 x 10-8)
would show 0.05 “significant” associations
1590 cases: 1590 controls.
25. study design considerations
Case-control or cohort
Sample size
Phenotype definition
Comparability of cases and controls
• Genotyping quality
• Population substructure
• Laboratory procedures, genotyping, data cleaning
26. population stratification
requires both allele frequency
and disease prevalence differences
Balding. Nature Reviews Genetics. 2006; 7:781-791
27. Q-Q plots
(modified from McCarthy et al.,Nature Reviews Genetics, May 2008)
28. Allele frequency & effect size
Feasibility of identifying genetic variants by risk allele
frequency and strength of genetic effect (odds ratio).
TA Manolio et al. Nature 461, 747-753 (2009) doi:10.1038/nature08494
29. reasons for larger sample size:
• More genotypes / tests • Lower effect size
• More genotype error or • Lower frequency of risk
misclassification allele
• Higher heterogeneity of • Lower correlation
association between marker allele
and risk allele.
31. Multi-stage discovery
Carry-forward a large number of
potential associations through
multiple, narrowing stages.
Protect against false positives via
replication
Minimize false negative results
via permissive early thresholds
From
Hoover,
R.
Epidemiology.
18(1):13-‐17,
January
2007.
32. Meta analysis
Combine results from several
studies to increase power using
traditional methods of meta-
analysis.
Allows for first stage discovery of
small effect sizes
34. Wellcome Trust Case Control Consortium
Biggest projects undertaken to identify genetic variation that
may be associated with disease
£ 9 million in funding from Wellcome Trust
GWAS of seven common diseases: 2,000 cases each and 3,000
shared controls
All genotyping data available to scientific community
www.wtccc.org.uk; (Nature, vol 447, 7 June 2007)
40. Coronary Disease GWAS: 9p21
author McPherson Helgadottir Samani Larson
where Science Science NEJM BMC Med Gen
when May 2007 May 2007 August 2007 Sept 2007
design 3-stage case-control case control cohort
case control
discovery OHS 1 deCODE: Iceland A WTCCC Framingham Heart
OHS 2 Study
ARIC
replication CCHS Iceland B German Family Study
DHS 3 U.S. case-control
OHS-3
case severe premature CHD MI MI or revascularization + incident MI
definition fhx of CAD
age at onset <60 <70 males <66
<75 females
41. 9p21 results
study SNP locus hazard/odds ratio PAR
ARIC rs10757274 9p21 AB: 1.18 (1.02-1.37) 12-15%
BB: 1.29 (1.09-1.52)
CCHS rs10757274 9p21 AB: 1.26 (1.12-1.42) 10-13%
BB: 1.38 (1.19-1.60)
deCODE rs10757278 9p21 AB: 1.26 (1.16-1.36) 21%
BB: 1.64 (1.47-1.82)
deCODE rs10757278 9p21 AB: 1.49 (1.31-1.69) 31%
early onset BB: 2.02 (1.72 - 2.36)
Helgadottir, Science 2007
McPherson, Science 2007
43. 9p21 locus
not located within a “gene”
region contains CDKN2A and CDKN2B genes
• role in cell proliferation, cell aging and apoptosis -
important features of atherogenesis
• Sequencing did not reveal obvious candidates
may implicate a previously unrecognized gene or regulatory
element
same region also associated with type 2 diabetes
53. missing heritability (2009)
% of heritability
number of loci explained
Age-related macular degeneration 5 50%
Crohn’s disease 32 20%
Type-2 diabetes 18 6%
HDL cholesterol 7 5%
Height 40 5%
Early-onset MI 9 2.8%
Fasting glucose 4 1.5%
Manolio, Nature 2009
54. missing heritability
many variants with small effects yet to be found
• larger sample sizes have revealed more loci
true positives below significance threshold
contribution of rare variants
failure to identify true causal variant
structural variants poorly captured by arrays
previous estimates of heritability flawed
GxG or GxE interactions
55. missing heritability (update)
Meta-analysis of > 100,000 discovers 59 new associations
SNPs explain ~12% of trait variability & ~ 25% heritability
Some predict MI risk; point to LDL/HDL differences
56. disease prediction
hope: highly predictive and affordable genetic tests
reality: low discriminatory and predictive ability Manolio, NEJM 2010
57. next steps
Ever larger sample sizes
Studies of non-European ethnic populations
Sequencing implicated genetic regions
More complex genetic models
• Gene x Gene interactions
• pooling of rare variants
Functional biology: work in basic science and animal models
58. summary
GWAS have led to new Don’t forget:
biology • case definition
Small effect sizes • QC measures
Not useful in prediction • sample size and power
Much yet to be discovered • multiple testing
More complicated than we • independent replication
thought
59. “There have been few, if any,
similar bursts of discovery in the
history of medical research”
-- “Drinking from the fire hose …” (Hunter & Knox)
64. Sources / References / Reading
1. The International HapMap Consortium.* A haplotype map of the human genome. Nature, 2005. 437(7063): p. 1299-320.[16255080].
2. The Type 1 Diabetes Genetics Consortium.* Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nature Genetics, 2009
May 10 [19430480]
3. Myocardial Infarction Genetics Consortium.* Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number
variants. Nat Genet. 2009 Mar;41(3):334-41 [19198609]
4. The Wellcome Trust Case Control Constortium.* Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 2007. 447
(7145): p. 661-78.[17554300].
5. Balding, D.J., A tutorial on statistical methods for population association studies. Nat Rev Genet, 2006. 7(10): p. 781-91.[16983374].
6. Christensen, K. and J.C. Murray, What genome-wide association studies can do for medicine. N Engl J Med, 2007. 356(11): p. 1094-7.[17360987].
7. Frazer, K.A., et al., A second generation human haplotype map of over 3.1 million SNPs. Nature, 2007. 449(7164): p. 851-61.[17943122].
8. Hirschhorn, J.N., et al., A comprehensive review of genetic association studies. Genet Med, 2002. 4(2): p. 45-61.[11882781].
9. Hoover, R. The evolution of epidemiologic research: from cottage industry to "big" science. Epidemiology. 2007 Jan;18(1):13-7. [17179754]
10. Hunter, D.J. and P. Kraft, Drinking from the fire hose--statistical issues in genomewide association studies. N Engl J Med, 2007. 357(5): p. 436-9.[17634446].
11. Li Y, Willer C, Sanna S, Abecasis G., Genotype imputation. Annu Rev Genomics Hum Genet. 2009;10:387-406. [19715440]
12. Johnson AD and O’Donnell CJ: Open access database of GWA results, BMC Medical Genetics 2009: 10:6
13. Manolio, T.A., et al., Genetics of ultrasonographic carotid atherosclerosis. Arterioscler Thromb Vasc Biol, 2004. 24(9): p. 1567-77.[15256397].
14. Manolio, T.A., L.D. Brooks, and F.S. Collins, A HapMap harvest of insights into the genetics of common disease. J Clin Invest, 2008. 118(5): p. 1590-605.[18451988].
15. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature. 2009 Oct 8;461(7265):747-53. [19812666]
16. Manolio, TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010 Jul 8;363(2):166-76. [20647212]
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